{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatic calling is: Smart\n"
     ]
    }
   ],
   "source": [
    "from scienv import *\n",
    "%matplotlib inline\n",
    "%autocall 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "from scipy import signal\n",
    "from collections import Counter\n",
    "import os\n",
    "imshow = plt.imshow\n",
    "gimshow = lambda p: plt.imshow(p, cmap='gray')\n",
    "mpl.rcParams['figure.figsize'] = (12,9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "pig = plt.imread(\"pig.png\")[...,:3]\n",
    "deer = plt.imread('deer.png')[...,:3]\n",
    "kenan = plt.imread('柯南.png')[...,:3]\n",
    "bat = plt.imread('bat.png')[...,:3]\n",
    "neteast = plt.imread('neteast.png')[...,:3]\n",
    "das = plt.imread('das.png')[...,:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------> imshow(das)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x18f2075be48>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow das"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 灰度化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "gray_w = np.r_[0.299, 0.587, 0.114]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "gray = lambda p: p @ gray_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x18f1ac1eb00>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gimshow(gray(das))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 中值滤波"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "medfilt = lambda p, ks=3: 1-signal.medfilt2d(1-p, kernel_size=ks) #背景色为1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x18f0b058cc0>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(medfilt(gray(das), 3), cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       ...,\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medfilt(gray(kenan))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 二值化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def square_error(d1, d2):\n",
    "    '返回二类聚类的平方损失判据，归一到0-1'\n",
    "    n1 = sum([i[-1] for i in d1])\n",
    "    n2 = sum([i[-1] for i in d2])\n",
    "    s1 = sum([k*v for k,v in d1])\n",
    "    s2 = sum([k*v for k,v in d2])\n",
    "    m1 = s1 / n1\n",
    "    m2 = s2 / n2\n",
    "    m0 = (s1 + s2) / (n1 + n2)\n",
    "    e1 = sum([(k-m1)**2*v for k,v in d1])\n",
    "    e2 = sum([(k-m2)**2*v for k,v in d2])\n",
    "    e0 = sum([(k-m0)**2*v for k,v in d1]) + sum([(k-m0)**2*v for k,v in d2])\n",
    "    return (e1 + e2) / e0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "def devide(pic):\n",
    "    '返回一维二类聚类的分界值，小于一类，大于等于一类'\n",
    "    p = pic.flatten()\n",
    "    c = sorted(Counter(p).items())\n",
    "    es = []\n",
    "    for i in range(1, len(c)-1):\n",
    "        es.append(square_error(c[:i], c[i:]))\n",
    "    ind = es.index(min(es))\n",
    "    return c[ind+1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def binarize(p, th='auto'): \n",
    "    if th == 'auto':\n",
    "        th = devide(p)\n",
    "        print(f'二值化阈值取：{th}')\n",
    "    return np.where(p<th, 0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def backcolor_of(pic):\n",
    "    '输入0-1二值图片，返回0或1代表背景色，判据是内聚度低为背景'\n",
    "    m = [np.array([0,0], np.float64), np.array([0,0], np.float64)]\n",
    "    s = [np.array([0,0], np.float64), np.array([0,0], np.float64)]\n",
    "    n = [0, 0]\n",
    "    for r in range(pic.shape[0]):\n",
    "        for c in range(pic.shape[1]):\n",
    "            i = pic[r, c]\n",
    "            m[i] += (r, c)\n",
    "            s[i] += (r**2, c**2)\n",
    "            n[i] += 1\n",
    "    e = [sum(s)/n-sum((m/n)**2) for m,s,n in zip(m,s,n)]\n",
    "    bc = 1 if e[0] < e[1] else 0\n",
    "    print(f'背景色为{bc}，两种颜色的平方误差分别为：{e}')\n",
    "    return bc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dist_plot(pic):\n",
    "    n = devide(gray(pic))\n",
    "    plt.subplot(121)\n",
    "    gimshow(pic)\n",
    "    plt.subplot(122)\n",
    "    sns.distplot(gray(pic).flatten(), vertical=True, kde=False)\n",
    "#     plt.xscale('log')\n",
    "    plt.axhline(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\programs\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist_plot(das)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------> gimshow(binarize(medfilt(gray(das))))\n",
      "二值化阈值取：0.4062274565696715\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x18f0c00cfd0>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gimshow binarize(medfilt(gray(das)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def strip(p, backgroundColor=1):\n",
    "    isbak = (p == backgroundColor)\n",
    "    p = p[~isbak.all(axis=1)]\n",
    "    p = p[:, ~isbak.all(axis=0)]\n",
    "    return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(p, kn=7, th='auto', set_bakc=1):\n",
    "    bp = binarize(gray(p), th)\n",
    "    bakc = backcolor_of(bp)\n",
    "    if bakc != 1:\n",
    "        p = 1 - p\n",
    "    fp = medfilt(gray(p), kn)\n",
    "    if 1 != set_bakc:\n",
    "        fp = 1 - fp\n",
    "    bp = binarize(fp, th)\n",
    "    return strip(bp, set_bakc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-134-31493af802a9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpreprocess\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdas\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mset_bakc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-18-9af2231e5403>\u001b[0m in \u001b[0;36mpreprocess\u001b[1;34m(p, kn, th, set_bakc)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mpreprocess\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'auto'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mset_bakc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mbp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbinarize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[0mbakc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbackcolor_of\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mbakc\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m         \u001b[0mp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-14-6c6aba1e7f9c>\u001b[0m in \u001b[0;36mbinarize\u001b[1;34m(p, th)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mbinarize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'auto'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'auto'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m         \u001b[0mth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdevide\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'二值化阈值取：{th}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m<\u001b[0m\u001b[0mth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-13-4b4c9c0185e2>\u001b[0m in \u001b[0;36mdevide\u001b[1;34m(pic)\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m         \u001b[0mes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msquare_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m     \u001b[0mind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mind\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-11-4a3cf4e1fd50>\u001b[0m in \u001b[0;36msquare_error\u001b[1;34m(d1, d2)\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mm2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ms2\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mn2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mm0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0ms1\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0ms2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mn1\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mn2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m     \u001b[0me1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mm1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mv\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0md1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m     \u001b[0me2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mm2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mv\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0md2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[0me0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mm0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mv\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0md1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mm0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mv\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0md2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-11-4a3cf4e1fd50>\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mm2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ms2\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mn2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mm0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0ms1\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0ms2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mn1\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mn2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m     \u001b[0me1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mm1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mv\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0md1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m     \u001b[0me2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mm2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mv\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0md2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[0me0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mm0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mv\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0md1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mm0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mv\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0md2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "imshow(preprocess(das, kn=3, set_bakc=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "二值化阈值取：0.861529420018196\n",
      "背景色为1，两种颜色的平方误差分别为：[6068.927631276587, 19814.876815384952]\n",
      "二值化阈值取：0.8929019671678542\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x18f08d48320>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(preprocess(deer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "二值化阈值取：0.8901960849761963\n",
      "背景色为1，两种颜色的平方误差分别为：[8214.405062046804, 31836.746153795786]\n",
      "二值化阈值取：0.9254902005195618\n"
     ]
    }
   ],
   "source": [
    "p = preprocess(kenan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 边缘检测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "kn = [[1,1,1],[1,0,1],[1,1,1]]\n",
    "def bound(p):\n",
    "    'the bound pixels is 1'\n",
    "    mp = signal.convolve2d(p, kn, mode='same', fillvalue=1)\n",
    "    mp = np.where((mp>=1)&(p==0), 1, 0)\n",
    "    return mp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "二值化阈值取：0.861529420018196\n",
      "背景色为1，两种颜色的平方误差分别为：[6068.927631276587, 19814.876815384952]\n",
      "二值化阈值取：0.8929019671678542\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x18f08d9d2e8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(bound(preprocess(deer)))\n",
    "#plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "二值化阈值取：0.861529420018196\n",
      "背景色为1，两种颜色的平方误差分别为：[6068.927631276587, 19814.876815384952]\n",
      "二值化阈值取：0.8929019671678542\n"
     ]
    }
   ],
   "source": [
    "p = bound(preprocess(deer))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 欧拉环游"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import graphHelper as gh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "idim = gh.to_id_image(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairs = gh.neiPairsOf(idim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2027]\n"
     ]
    }
   ],
   "source": [
    "G = nx.Graph()\n",
    "\n",
    "G.add_edges_from(pairs)\n",
    "\n",
    "largest_cc = max(nx.connected_components(G), key=len)\n",
    "print([len(c) for c in nx.connected_components(G)])\n",
    "G = G.subgraph(largest_cc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2027"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "odd = {v:d for v,d in G.degree if d%2==1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "nds = list(odd.keys())\n",
    "paths = []\n",
    "for i in range(0, len(nds), 2):\n",
    "    paths.append(nx.shortest_path(G, nds[i], nds[i+1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathG = nx.MultiGraph()\n",
    "for p in paths:\n",
    "    pathG.add_path(p)\n",
    "#pathG.edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "pegs = [e[:2] for e in pathG.edges]\n",
    "pegs = [e if e[0]<=e[1] else e[::-1] for e in pegs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = Counter(pegs)\n",
    "pegs = [k for k,v in c.items() if v%2==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathG = nx.Graph()\n",
    "pathG.add_edges_from(pegs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "cycles = nx.cycle_basis(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cycle in cycles:\n",
    "    cyG = nx.Graph()\n",
    "    nx.add_cycle(cyG, cycle)\n",
    "    cyE = {e if e[0]<=e[1] else e[::-1] for e in cyG.edges}\n",
    "    in_cycle = cyE & pathG.edges\n",
    "\n",
    "    if len(in_cycle) > len(cyE)/2:\n",
    "#         print(in_cycle, len(in_cycle))\n",
    "#         print(cyE, len(cyE))\n",
    "#         print(cyE-in_cycle, len(cyE-in_cycle))\n",
    "#         print('\\n')\n",
    "        pathG.remove_edges_from(in_cycle)\n",
    "        pathG.add_edges_from((cyE-in_cycle))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------> len(pathG.edges)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len pathG.edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_accepted_len = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "mG = nx.MultiGraph(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "mG.add_edges_from(pathG.edges);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "odd2 = {v:d for v,d in mG.degree if d%2==1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odd2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.is_eulerian(mG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "eulerian_circuit = [p[0] for p in nx.eulerian_circuit(mG)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Chinese_Postman(G, min_accepted_len = 5):\n",
    "    odd = {v:d for v,d in G.degree if d%2==1}#奇数度顶点\n",
    "    nds = list(odd.keys())\n",
    "    paths = []\n",
    "    for i in range(0, len(nds), 2):\n",
    "        paths.append(nx.shortest_path(G, nds[i], nds[i+1]))#两两配对画最短路\n",
    "    pathG = nx.MultiGraph()\n",
    "    for p in paths:\n",
    "        pathG.add_path(p)#所有path组成一个图\n",
    "    pegs = [e[:2] for e in pathG.edges]\n",
    "    pegs = [e if e[0]<=e[1] else e[::-1] for e in pegs]\n",
    "    c = Counter(pegs)\n",
    "    pegs = [k for k,v in c.items() if v%2==1] #最多出现一次，出现偶数次对消  \n",
    "    pathG = nx.Graph()\n",
    "    pathG.add_edges_from(pegs)\n",
    "    \n",
    "    cycles = nx.cycle_basis(G)\n",
    "    for cycle in cycles:\n",
    "        cyG = nx.Graph()\n",
    "        nx.add_cycle(cyG, cycle)\n",
    "        cyE = {e if e[0]<=e[1] else e[::-1] for e in cyG.edges}\n",
    "        in_cycle = cyE & pathG.edges\n",
    "        if len(in_cycle) > len(cyE)/2: #圈中有超出一半的边要添加则取反\n",
    "            pathG.remove_edges_from(in_cycle)\n",
    "            pathG.add_edges_from((cyE-in_cycle))\n",
    "            \n",
    "    mG = nx.MultiGraph(G)#给G添重边\n",
    "    mG.add_edges_from(pathG.edges)\n",
    "    eulerian_circuit = [p[0] for p in nx.eulerian_circuit(mG)]\n",
    "    return eulerian_circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_eulerian_circuit(p, min_accepted_len=5):\n",
    "    idim = gh.to_id_image(p)\n",
    "    pairs = gh.neiPairsOf(idim)\n",
    "    G = nx.Graph()\n",
    "    G.add_edges_from(pairs)\n",
    "    largest_cc = max(nx.connected_components(G), key=len)\n",
    "    G = G.subgraph(largest_cc) #只取图中最大的连续边缘\n",
    "    euler_cir = Chinese_Postman(G, min_accepted_len)\n",
    "    return euler_cir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_xy(ids, shape):\n",
    "    coords = gh.toCoords(ids, shape)\n",
    "    y, x = coords.T\n",
    "    y = p.shape[0] - y\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "二值化阈值取：0.861529420018196\n",
      "背景色为1，两种颜色的平方误差分别为：[6068.927631276587, 19814.876815384952]\n",
      "二值化阈值取：0.8929019671678542\n"
     ]
    }
   ],
   "source": [
    "p = bound(preprocess(deer))\n",
    "ec = find_eulerian_circuit(p)\n",
    "ec = np.array(ec)\n",
    "ec = np.roll(ec, -ec.argmin()) #第一个点在左上角\n",
    "x, y = to_xy(ec, p.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-11.850000000000001, 248.85, -13.4, 303.4)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x, y)\n",
    "plt.axis('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2036"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace = x + 1j * y\n",
    "tl = trace.shape[-1]\n",
    "sp = np.fft.fft(trace)\n",
    "freq = np.fft.fftfreq(tl, d=1/tl)\n",
    "\n",
    "len(sp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62 0.9999390302917878\n"
     ]
    }
   ],
   "source": [
    "th = np.percentile(abs(sp), 97)\n",
    "mask = abs(sp) >= th\n",
    "print(mask.sum(), sum(abs(sp[mask])**2)/sum(abs(sp)**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp2 = sp.copy()\n",
    "sp2[~mask]=0\n",
    "\n",
    "iff = np.fft.ifft(sp2)\n",
    "\n",
    "o = sp[0]/tl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-9.805251545027694, 247.37518914063722, -11.957768433607914, 302.068988508005)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot(x, y, 'k')\n",
    "plot(iff.real, iff.imag, lw=5, alpha=0.8)\n",
    "plot(o.real, o.imag, 'o')\n",
    "plt.axis('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,\n",
       "        11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,\n",
       "        22.,  24.,  25.,  26.,  27.,  29.,  30.,  32.,  34., -39., -33.,\n",
       "       -32., -31., -30., -29., -28., -27., -25., -23., -22., -21., -20.,\n",
       "       -19., -18., -17., -16., -15., -14., -13., -12., -11., -10.,  -9.,\n",
       "        -8.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<StemContainer object of 3 artists>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4AAA0JIADAEBDAjgAADQkgAMAQEMCOAAANCSAAwBAQwI4AAA0JIADAEBDAjgAADQkgAMAQEMCOAAANCSAAwBAQwI4AAA0JIADAEBDAjgAADQkgAMAQEMCOAAANCSAAwBAQwI4AAA0JIADAEBDAjgAADQkgAMAQEMCOAAANCSAAwBAQwI4AAA0JIADAEBDAjgAADQkgAMAQEMCOAAANCSAAwBAQwI4AAA0JIADAEBDAjgAADQkgAMAQEMCOAAANCSAAwBAQwI4AAA0tGyUQaWUDyS5prv52VrrH5RSrkhyW5JVST5da93Yjb0gyV1J1iR5OMm7aq37SylnJdmU5OVJapLra63PlFJOT/LJJK9OsjPJNbXWHaWUFUk+nuS1SX6U5Lpa6/Ze9hoAAObI0CPgXdD+tSQ/n+SCJL9YSnlbkruTXJnk3CQXllI2dA/ZlOS9tdZzkkwkeWdXvyPJHbXW9Um+kuSWrv7BJFtrrecmuTPJR7r6byd5tqu/L8k9Y+wnAADMC6MsQXk6ye/WWvfWWvcl+WaSc5J8q9b6eK11fwah++pSyiuTrKq1fql77D1dfXmSy5LcP73eff3GDI6AJ8l9STZ045+v11ofTjLZHUUHAIAFa2gAr7VumwrUpZSfzmApysEMgvmUp5O8IsmZx6i/LMnuLqxPr2f6Y7r7dyeZPM62AABgwRppDXiSlFLOS/LZJL+fZH8GR8GnTGQQypckOTRCPV19asx0w7Y1krVrTx116IxNTq4+YdtebPSyP3rZL/0c3/IVS5PoZZ/0sl/62R+9nJlRT8J8fZIHkryv1vqpUsrlSc6YNmRdkqeSPHmM+veSnFZKWVprPdCNeaob891u3JOllGVJVifZNW1bjx2xrZHs2vVMDh48MvOPb3JydXbu/GHv212M9LI/etkv/ezHvr0HsnzFUr3siddlv/SzP4u5l0uWTMzqoO8oJ2H+ZJK/zuAqJJ/qyl8e3FVeU0pZmuS6JJtrrU8kea4L7Eny9q6+L8nWJNd29RuSbO6+fqi7ne7+rd345+ullEuSPFdr/c6M9xAAAOaRUY6A/16SU5LcVkqZqv15kpsyOCp+SgZheeoEy+uT3FlKWZPkq0lu7+rvTnJvKWVjku8keVtXvyXJPaWUbUn+sXt8knw0yV909T0ZhHkAAFjQhgbwWuvvJPmdY9x9/lHGfz3JRUepP5HkDUepfz/Jm49Sfy7JjcPmBwAAC4lPwgQAgIYEcAAAaEgABwCAhgRwAABoSAAHAICGBHAAAGhIAAcAgIYEcAAAaEgABwCAhgRwAABoSAAHAICGBHAAAGhIAAcAgIYEcAAAaEgABwCAhgRwAABoSAAHAICGBHAAAGhIAAcAgIaWzfUEAFh4Htm2Iw9ueSy7du/J2jUrc9XlZ+fi89bN9bQAFgQBHIAZeWTbjty7eXv27j+YJNm1e0/u3bw9SYRwgBFYggLAjDy45bHnw/eUvfsP5sEtj83RjAAWFgEcgBnZtXvPjOoAHE4AB2BG1q5ZOaM6AIcTwAGYkasuPzsrlh3+42PFsiW56vKz52hGAAuLkzABmJGpEy0/8dA3s//AIVdBAZghARyAGbv4vHV5+GtPJUnef/0vzPFsABYWS1AAAKAhARwAABoSwAEAoCEBHAAAGhLAAQCgIQEcAAAaEsABAKAhARwAABoSwAEAoCEBHAAAGhLAAQCgIQEcAAAaEsABAKAhARwAABoSwAEAoCEBHAAAGhLAAQCgIQEcAAAaEsABAKAhARwAABoSwAEAoCEBHAAAGhLAAQCgIQEcAAAaEsABAKAhARwAABoSwAEAoKFlow4spaxJ8sUkb6q1fruU8okklyR5thtya631M6WUK5LclmRVkk/XWjd2j78gyV1J1iR5OMm7aq37SylnJdmU5OVJapLra63PlFJOT/LJJK9OsjPJNbXWHePvMgAAzJ2RjoCXUn4pyReSnDOt/Nokl9VaL+j++0wpZVWSu5NcmeTcJBeWUjZ04zcleW+t9ZwkE0ne2dXvSHJHrXV9kq8kuaWrfzDJ1lrruUnuTPKR2e4kAADMF6MuQXlnkvckeSpJSikvSXJWkrtLKd8opdxaSlmS5KIk36q1Pl5r3Z9B6L66lPLKJKtqrV/qtndPV1+e5LIk90+vd1+/MYMj4ElyX5IN3XgAAFiwRgrgtdZ31Fq3TiutS/L5JL+R5HVJLk3ym0nOTPL0tHFPJ3nFceovS7K7C+vT65n+mO7+3UkmR90xAACYj0ZeAz5drfX/SfKWqdullI8muSGDI9mHpg2dSHIwg6A/Sj1dfWrMdBPT7htq7dpTRx06Y5OTq0/YthcbveyPXvZLP4dbvmJpkmP3atj9zJxe9ks/+6OXMzOrAF5K+dkk59RaH+hKE0n2JXkyyRnThq7LYNnKserfS3JaKWVprfVAN+apbsx3u3FPllKWJVmdZNeoc9y165kcPHhkth/f5OTq7Nz5w963uxjpZX/0sl/6OZp9ew8kyTF7tW/vgSxfsVQve+J12S/97M9i7uWSJROzOug728sQTiT5cCnlpd267JuTfCbJl5OUUsprSilLk1yXZHOt9Ykkz5VSXt89/u1dfV+SrUmu7eo3JNncff1Qdzvd/Vu78QAAsGDNKoDXWr+R5E+S/F2SR5N8rdZ6X631uSQ3JXmgq2/PCydYXp/kQ6WU7UlOTXJ7V393kptLKY9msJZ8Y1e/JcnrSinbujHvmc1cAQBgPpnREpRa66umfX1HBpcQPHLM55Kcf5T61zO4SsqR9SeSvOEo9e8nefNM5gcAAPOdT8IEAICGBHAAAGhIAAcAgIYEcAAAaEgABwCAhgRwAABoSAAHAICGBHAAAGhIAAcAgIYEcAAAaEgABwCAhgRwAABoSAAHAICGBHAAAGhIAAcAgIYEcAAAaEgABwCAhgRwAABoSAAHAICGBHAAAGhIAAcAgIYEcAAAaEgABwCAhgRwAABoSAAHAICGBHAAAGhIAAcAgIYEcAAAaEgABwCAhgRwAABoSAAHAICGBHAAAGhIAAcAgIYEcAAAaEgABwCAhgRwAABoSAAHAICGBHAAAGhIAAcAgIYEcAAAaEgABwCAhgRwAABoSAAHAICGBHAAAGhIAAcAgIYEcAAAaEgABwCAhgRwAABoSAAHAICGBHAAAGhIAAcAgIYEcAAAaEgABwCAhgRwAABoSAAHAICGlo0yqJSyJskXk7yp1vrtUsoVSW5LsirJp2utG7txFyS5K8maJA8neVetdX8p5awkm5K8PElNcn2t9ZlSyulJPpnk1Ul2Jrmm1rqjlLIiyceTvDbJj5JcV2vd3tteAwDAHBl6BLyU8ktJvpDknO72qiR3J7kyyblJLiylbOiGb0ry3lrrOUkmkryzq9+R5I5a6/okX0lyS1f/YJKttdZzk9yZ5CNd/beTPNvV35fknjH2EQAA5o1RlqC8M8l7kjzV3b4oybdqrY/XWvdnELqvLqW8MsmqWuuXunH3dPXlSS5Lcv/0evf1GzM4Ap4k9yXZ0I1/vl5rfTjJZHcUHQAAFrShS1Bqre9IklLKVOnMJE9PG/J0klccp/6yJLu7sD69fti2uqUqu5NMHmdb3xlxv7J27amjDp2xycnVJ2zbi41e9kcv+6Wfwy1fsTTJsXs17H5mTi/7pZ/90cuZGWkN+BGWJDk07fZEkoMzqKerT42Zbti2RrZr1zM5ePDIpx3f5OTq7Nz5w963uxjpZX/0sl/6OZp9ew8kyTF7tW/vgSxfsVQve+J12S/97M9i7uWSJROzOug7m6ugPJnkjGm312WwPOVY9e8lOa2UsrSrn5EXlrN8txuXUsqyJKuT7DrOtgAAYEGbTQD/cpJSSnlNF6qvS7K51vpEkudKKa/vxr29q+9LsjXJtV39hiSbu68f6m6nu39rN/75einlkiTP1VpHXn4CAADz1YwDeK31uSQ3JXkgyaNJtueFEyyvT/KhUsr2JKcmub2rvzvJzaWUR5NcmmRjV78lyetKKdu6Me/p6h9NsrKr355BmAcAgAVv5DXgtdZXTfv6c0nOP8qYr2dwlZQj608kecNR6t9P8uaj1J9LcuOocwMAgIXCJ2ECAEBDAjgAADQkgAMAQEMCOAAANCSAAwBAQwI4AAA0JIADAEBDAjgAADQkgAMAQEMCOAAANCSAAwBAQwI4AAA0JIADAEBDAjgAADQkgAMAQEMCOAAANCSAAwBAQwI4AAA0JIADAEBDAjgAADQkgAMAQEMCOAAANCSAAwBAQwI4AAA0JIADAEBDAjgAADQkgAMAQEMCOAAANCSAAwBAQwI4AAA0JIADAEBDAjgAADQkgAMAQEMCOAAANCSAAwBAQwI4AAA0JIADAEBDAjgAADQkgAMAQEMCOAAANCSAAwBAQwI4AAA0JIADAEBDAjgAADQkgAMAQEMCOAAANCSAAwBAQwI4AAA0JIADAEBDy+Z6AgAsPo9s25EHtzyWXbv3ZO2albnq8rNz8Xnr5npaAE0I4AA09ci2Hbl38/bs3X8wSbJr957cu3l7kgjhwKJgCQoATT245bHnw/eUvfsP5sEtj83RjADaEsABaGrX7j0zqgOcbARwAJpau2bljOoAJ5ux1oCXUv42ycuT7OtK/yrJ2Uk2Jlme5MO11o91Y69IcluSVUk+XWvd2NUvSHJXkjVJHk7yrlrr/lLKWUk2dduvSa6vtT4zznwBmHtXXX72YWvAk2TFsiW56vKz53BWAO3M+gh4KWUiyTlJzq+1XlBrvSDJk0n+OMklSS5IcnMp5WdKKauS3J3kyiTnJrmwlLKh29SmJO+ttZ6TZCLJO7v6HUnuqLWuT/KVJLfMdq4AzB8Xn7cuN25Yn2VLJ5IMjnzfuGG9EzCBRWOcI+Cl+///WUpZm+TOJD9M8vla6/eTpJRyf5K3JtmS5Fu11se7+qYkV5dSHk2yqtb6pW5b9yS5tZRyV5LLkvz6tPqWJO8fY74AzBMXn7cuD3/tqSTJ+6//hTmeDUBb46wBf2mSzyV5S5JfTfKuJGcleXramKeTvCLJmTOsvyzJ7lrr/iPqAACwoM36CHit9ZEkj0zdLqV8PIM13h+cNmwiycEMgv6hMerp6iNbu/bUmQyfkcnJ1Sds24uNXvZHL/uln8MtX7E0ybF7Ne79vJhe9Us/+6OXMzPrAF5KuSTJylrr57rSRJJvJzlj2rB1SZ7KYG34TOrfS3JaKWVprfVAN+apmcxv165ncvDgkRl+fJOTq7Nz5w973+5ipJf90ct+6edo9u09kCTH7NW+vQeyfMXS495/vMdzOK/LfulnfxZzL5csmZjVQd9xlqCcnuTPSimnlFJWJ7kxyf+Q5FdLKZOllJck+ZdJ/lOSLycppZTXlFKWJrkuyeZa6xNJniulvL7b5tu7+r4kW5Nc29VvSLJ5jLkCAMC8MM4SlP9YSvmlJP+QZGmSj9Va/66U8m+T/G2SFUnuqrX+fZKUUm5K8kCSU5I8lOT+blPXJ7mzlLImyVeT3N7V353k3lLKxiTfSfK22c4VgIXnkW078uCWx7Jr956sXbMyV11+tiulACeFsa4DXmu9JUdcHrDW+pdJ/vIoYz+X5Pyj1L+e5KKj1J9I8oZx5gfAwvTIth2HXSt81+49uXfz9iQRwoEFzydhAjDvPLjlscM+qCdJ9u4/mAe3PDZHMwLojwAOwLyza/eeGdUBFhIBHIB5Z+2alTOqAywkAjgA885Vl5+dFcsO/xG1YtmSXHX52XM0I4D+jHUSJgCcCFMnWn7ioW9m/4FDroICnFQEcADmpYvPW5eHvzb4DLb3X/8LczwbgP5YggIAAA05Ag7ASckH+QDzlQAOwEnHB/kA85klKACcdHyQDzCfCeAAnHR8kA8wnwngAJx0fJAPMJ8J4ACcdHyQDzCfOQkTgJOOD/IB5jMBHICTkg/yAeYrS1AAAKAhARwAABoSwAEAoCEBHAAAGhLAAQCgIVdBAVhAHtm2Iw9ueSy7du9xaT2ABUoAB1ggHtm2I/du3p69+w8mGXys+r2btyeJEA6wgFiCArBAPLjlsefD95S9+w/mwS2PzdGMAJgNARxggdi1e8+M6gDMTwI4wAKxds3KGdUBmJ8EcIAF4qrLz87amaUEAAAORklEQVSKZYe/ba9YtiRXXX72HM0IgNlwEibAAjF1ouUnHvpm9h84dMyroLhSCsD8JoADLCAXn7cuD3/tqSTJ+6//hRfd70opAPOfJSgAJxFXSgGY/wRwgJOIK6UAzH8COMBJxJVSAOY/ARzgJOJKKQDzn5MwAU4io14p5XhcRQXgxBLAAU4yw66UcjyuonI4v4wAJ4IlKAA8z1VUXjD1y8jUCaxTv4w8sm3HHM8MWOgcAQdYZI53VNdVVF5wvF9GHAUHxiGAAywiw5aYrF2z8qhhezFeRWWUX0YsUQFmwxIUgEVk2BITV1F5wbBLOlqiAsyWAA6wiAw7qnvxeety44b1WbZ0IskgbN64Yf2iPKo77JcR6+WB2bIEBWARGWWJyThXUTmZDLuko/XywGwJ4ADzyIleU3zV5WcftgY8WbxLTEZxvF9GrJcHZssSFIB5osWaYktM+mO9PDBbjoADzBOtLntniUk/+vjUUWBxEsABejLu8hFrihcev8wAsyGAA4vCiV5b3cdHuFtTzGy5HjksLNaAAye9Fmur+7gknTXFzIbrkcPC4wg4cNJrsba6j+Uj1hTPzGI56ju1n9/fvSc/fpT9bHXuANAfARw46bVYW93X8hFrikfTx5KfVsb5RWGU/ezr9b1YfqGB+UAABxaEccJBi7XVrq/dVl9Hfef7uQGj7Gcfr+8+fqER4GF0Ajgw740bDlqEY8tH2urjqG+Lo+jj/qIwyn6O8voeFo7HneeovRTSYUAAB+ZcH+HgeNsYNRwPW2s7jOUj7fRx1HchnBswyn4Oe323WMYy6vfoQlk2BCeaAA7z2GI4WtRHOBhlG8PCsXCwsPTxV42FcG7AqPt5vNd3i2Uso/TSyaLwAgEc5qlRAuG4R2xHnce4vwQcbxt9hIM+frCPe5SdtvpY8jNfzg3o4683x9PXMpbjGaWXrT5oarF8ny6W/TxZCeAc5kSHrYUyh/mwH8MCYYs1l308x7Bt9BEO+vjB3sdRdtoad8nPfDg3YCZ/vVm+Ymn+9dXnz3gOfSxjmZrrsb7PR+llXyeLHu/9rI8DFy2C7SjPMc77aqv96MNCmWff5nUAL6Vcl2RjkuVJPlxr/dgcT+mYToYXUB/f0OO++bUIfH31oo/HH28/hgXCvtZcjnt0ethzDNtGH+Ggjx/sLY6yM7+0OnF23OUh4+pjGcuw7/NRejnuXwNGeT8b98DFfDmwMe77al8HDEb5mT/OX2FbzXM+mrefhFlK+Ykkf5zkkiQXJLm5lPIzczuroztZPoVs2Cf5jbKfM9nGoaNsY5RPExw2jz4+kXDcbfSxH8cKj1P1cddcjjKHPp5j2DZG/fTHi89bl7PPPC3lJ0/Pn7379S/6wT7uJ0gO20arP5/T1vFeVy20eF1dfN663LhhfZYtnUgyeA+5ccP63v4iN/15jtfLYfPo4719nAMXoz7HuD/zR3mOcd9X+/g5OGw/h/08H0WLec5X8zaAJ7kiyedrrd+vtT6b5P4kb53jOR1VHy+g+aCPb+hxt9Ei8I1i3G30sR/DAuGwgD7KPIbNoY/nGLaNPsJBi22M0guYqVavq3F/0ejrF4XjzaOP9/ZxD1z08d49zCjPMe77ah//Xn38sjJMi3nOVxOHDh2a6zkcVSnl3yT5sVrrxu72O5JcVGu9echDX5Xk8V27nsnBg/3v2+Tk6uzc+cPDar/x7z6fJPnnux/Lz+3+b4fdV846vfc5nCiPPbU7+494ESfJsmVLcvaZa1K/84/HfOzUfo67jWGPT9LLNoYZdxt97EeS7P6nvdmx659y6NDgsZOnn5I1L1nxovumTEwk69a+5Pkx4/579PEco2wjSb7zvWeSJGe9/NRjzmnYmHHvnxqzZGIir5j8scPqfe7HMH3txzjb6Os5jtbLvp9jnPvn+jlm8ro6Xi9P9DxHfU8c5zn6eG8f1s9h2+jrvft4RnmOcd9X+/g5OGw/x+1DMv7P2uTweX5jzWvyX9e88JfPu//HXxlpG+NYsmQia9eemiQ/leTboz5uPq8BX5JkeoKeSPLif6Vj6JpxQkxOrj789ktXZecPfvSiccuXL8nyFfO5xYdbt/Yl+e7OZ3No2i8uE0smsm7tS7J8xbIsX74k+/a9+J9g+n6Ou41hj58aO+42xu1FH48fpZ9rVyzL0qWDo+Cnrz7lsHFT9z39/z6bAwcOZfnyJflnP/6Sw8aN++/Rx3OMso0kWXva4Pbx+jtszLj3H29Mn/sxzIncD88xv55jPn1/HG/MqO+J4zxHH+/tw/o5bBt9vXcfzyjPMe77ah8/B4ft57h9ONHznHzpqhfltflkPqfDJ5NcOu32uiRPjfrglkfAf/2Sn8q9m7fnv645+/nfvFYsW5IbN6zPunl+EsB065L84CgnMqzv9uHxbTty31FOoJm+n+NuY9jj+9rGuL3o4/Gj9HNqW8d7nvU5+utylHmM+m+6fsx9HbaNYfs56phx758ac7x+9rEfo8xh3Odo1ath9x+rl30+xzj3z4fnGPV1dbxejjKPceY56nviOM/R13v78fo5fRvTTxxcP+LPsFHmOcr+j/KeOc77ah8/B4ft57h9ONHz/PVLfuq43y99mXYEfEbm8xKUn0jyhSQXJXk2yReT3Fxr/fshD31VGi9BSRbmGbiz0efl/ca5dvXJ0u++9mPYD+YWcziZjNNPDqeX/VkMvWz5fuR9c7gTfRWUVvM8kWa7BGXeBvDk+csQ/k9JViS5q9b670d42KsyBwGcmdPL/uhlv/SzP3rZH73sl372ZzH38mRcA55a618m+cu5ngcAAPRlPl+GEAAATjoCOAAANCSAAwBAQwI4AAA0JIADAEBDAjgAADQkgAMAQEMCOAAANCSAAwBAQwI4AAA0JIADAEBDAjgAADQkgAMAQEMCOAAANCSAAwBAQwI4AAA0tGyuJ3ACLE2SJUsmTtgTnMhtLzZ62R+97Jd+9kcv+6OX/dLP/izWXk7b76UzedzEoUOH+p/N3Lokyda5ngQAAIvGpUm+MOrgkzGAr0xyYZKnkxyY47kAAHDyWprkjCT/OcmeUR90MgZwAACYt5yECQAADQngAADQkAAOAAANCeAAANCQAA4AAA0J4AAA0JAADgAADZ2MH0V/wpRSfj7Jl2qtK7vbK5J8PMlrk/woyXW11u1zOMV5r5RyaZIPJ1mR5PEkN9Zaf1BKOT3JJ5O8OsnOJNfUWnfM3UwXhlLK65N8KIN+7kryG7XWJ/Rz9kop/3OSA7XWP+xu6+UslVKuS7IxyfIkH661fmyOp7TglFLWJPlikjfVWr9dSrkiyW1JViX5dK1145xOcIEopXwgyTXdzc/WWv9AL2evlPJHSd6a5FCSj9dab9PPmXEEfESllJck+WgGQWfKbyd5ttZ6bpL3JblnDqa20HwiydtrrT+b5NEkv9/VP5hka9fLO5N8ZI7mt9B8Msk7aq0XdF/f3tX1c4ZKKaeVUj6e5HePuEsvZ6GU8hNJ/jjJJUkuSHJzKeVn5nZWC0sp5Zcy+Gjrc7rbq5LcneTKJOcmubCUsmHuZrgwdMHw15L8fAavxV8spbwtejkrpZTLk/xKkp/L4ADkb5VSzo9+zogAPrr/NYMjt9O9MYPQk1rrw0kmSylntZ7YAnNurfXRUsryJD+R5Add/fleJrkvyYZuDMdQSlmZZGOt9Rtd6RtJpl5/+jlzVyb5Vgbf69Pp5exckeTztdbv11qfTXJ/BkfMGN07k7wnyVPd7YuSfKvW+nitdX+STUmunqvJLSBPJ/ndWuveWuu+JN/M4JcavZyFWuuWJL/c9e3lGaymOD36OSMC+AhKKW9O8pJa6/1H3HVmBt/YU55O8opmE1uAaq37Sik/m+TJJL+c5FPdXc/3svvm3Z1kck4muUDUWvfUWjclSSllSZI/TPLX3d36OUO11v9Qa/13SQ4ccZdezo73xzHVWt9Ra906raSns1Br3VZr/VKSlFJ+OoOlKAejl7PW/Sy/NYO/ZH8uXpszZg34NKWUqzNYTzvd9iRrMjiac6QlGax/mjKRwTf1onesXtZar6i1/t9J/lkp5V8l+XSS/y6D3k2nl9Mcr5/duQj3ZvD9/L909+nnMRyvl8d4iF7OjvfH/unpGEop5yX5bAZLH/enW9rT0csZqrV+oJTyp0n+JoNeem3OgAA+Ta31r5L81fRaKeUdSf5NkodLKVO1ryW5NIOjuGckeawbvi4v/KlwUTtGL08ppfx6rXXqKO2mvPDn/u9m0L8nSynLkqzO4KRCcvR+Jkkp5dQk/0cGvbqy+/Nqop/HdKxeHodezs6TGbxPTvH+OL6pnzlT9HRE3QnrDyR5X631U906Zr2chVLK+iSn1Fq/Vmv9p1LKgxksL5v+10P9HMISlCFqrXfVWs+utV7QneiW7usfJnkoyQ1JUkq5JMlztdbvzOF057t9ST5WSvnF7vY1GZxglEzrZZJrMzjpbV8YZlOS/5bk2lrrnml1/eyPXs7O/5XkV0spk91J7P8yyX+a4zktdF9OUkoprymlLE1yXZLNczynea+U8pMZLM+7rtY6texRL2fv1UnuLKWs7P4Ce2WSv4h+zogj4OP5aJK/KKVsS7InydvneD7zWq31QCnl2iT/e/cN+t0k7+juviXJPV0v/zHJ9XM0zQWjuyzmlRmswftq9xeap2qt/yL62Se9nIVa63dLKf82yd9mcPWou2qtfz/H01rQaq3PlVJuyuBI7ikZ/HJ45LlJvNjvZdCv26b+kp3kz5PcFL2csVrrQ6WUi5L8QwZHvR/o/qqwM/o5solDhw4NHwUAAPTCEhQAAGhIAAcAgIYEcAAAaEgABwCAhgRwAABoSAAHAICGBHAAAGhIAAcAgIb+f0E752iUY2c1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.stem(freq[mask], abs(sp[mask]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## animate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.patches import Circle\n",
    "from matplotlib.collections import PatchCollection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_circles(ax, cs):\n",
    "    p = PatchCollection(cs, edgecolors='0.2', facecolors='none', linewidths=1)\n",
    "    ax.add_collection(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "patches=[]\n",
    "for i in range(1,4):\n",
    "    circle = Circle((0, i), i)\n",
    "    patches.append(circle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-3.3, 3.3, -0.30000000000000004, 6.3)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()    \n",
    "add_circles(ax, patches)\n",
    "ax.axis('scaled')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "w0 = 2 * np.pi / tl\n",
    "fr2 = freq[mask]\n",
    "sp2 = sp[mask]/tl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "si = np.argsort(abs(sp2)[1:])[::-1]\n",
    "si = [0, *(si+1)]\n",
    "fr2 = fr2[si]\n",
    "sp2 = sp2[si]\n",
    "maxfre = int(max(abs(fr2)))\n",
    "n_frame = 4 * maxfre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = np.c_[:n_frame]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = sp2 * np.exp(1j*i*fr2/n_frame*np.pi*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(156, 62)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "rr = abs(frame)[:,1:]\n",
    "oo = frame.cumsum(axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cirs(rr, oo):\n",
    "    circles=[]\n",
    "    for rf, of in zip(rr, oo):\n",
    "        cirs = []\n",
    "        for ri, oi in zip(rf, of):\n",
    "    #         print(of.real, of.imag)\n",
    "            circle = Circle((oi.real, oi.imag), ri)\n",
    "            cirs.append(circle)\n",
    "        circles.append(cirs)\n",
    "    return circles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "circles = get_cirs(rr, oo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "xm, xM = iff.real.min(), iff.real.max()\n",
    "ym, yM = iff.imag.min(), iff.imag.max()\n",
    "xs, ys = xM - xm, yM - ym\n",
    "r = 0.15\n",
    "xl, xr = xm-r*xs, xM+r*xs\n",
    "yb, yt = ym-r*ys, yM+r*ys\n",
    "figsize=(10, 10*(yt-yb)/(xr-xl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_frame(ax, i, circles, oo, trace, n_frame):\n",
    "    cs = circles[i]\n",
    "    os = oo[i]   \n",
    "    add_circles(ax, cs)\n",
    "    ax.plot(os.real, os.imag, 'k')\n",
    "    ax.plot(os.real, os.imag, 'k.')\n",
    "    ax.plot(os.real[-1], os.imag[-1], 'o', c='#e74c3c')\n",
    "    ax.plot(os.real[0], os.imag[0], 'ko')\n",
    "    ti = int(i/n_frame*len(trace))\n",
    "    plot(trace.real[:ti], trace.imag[:ti], lw=5, alpha=1, c='#e74c3c')\n",
    "    ax.axis('equal')\n",
    "    ax.axis('off')\n",
    "    ax.set_xlim(xl, xr)\n",
    "    ax.set_ylim(yb, yt)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18f0bfd1e48>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x879.146 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=figsize)\n",
    "plot_frame(ax, 72, circles, oo, iff, n_frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from io import BytesIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x879.146 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bufs = []\n",
    "circles = get_cirs(rr, oo)\n",
    "fig, ax = plt.subplots(figsize=figsize)\n",
    "for i in range(n_frame):\n",
    "    buf = BytesIO()\n",
    "    fig, ax = plt.subplots(figsize=figsize)\n",
    "    plot_frame(ax, i, circles, oo, iff, n_frame)\n",
    "    fig.savefig(buf)\n",
    "    bufs.append(buf)\n",
    "    plt.close(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "def saveGIF(fn, ims, duration=250):\n",
    "    ims[0].save(fn, save_all=True, append_images=ims[1:], duration=duration,\n",
    "                        optimize=True, minimize_size=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "ims = [Image.open(buf) for buf in bufs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "saveGIF('aa.gif', ims, duration=30000/n_frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
